Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

Gao, Xiaoyu; Gao, Shanhong

doi:10.1155/2020/8816185

Cited by 7 publications

(8 citation statements)

References 53 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The information of fog occurrence in numerical weather forecast highly depends on RH values, and thus uncertainty in RH means inaccurate fog forecast. Data assimilation is needed for coastal sea fog simulation in the future, and relevant work is already underway [40,41].…”

Section: Discussionmentioning

confidence: 99%

Impacts of Sea–Land Breeze Circulation on the Formation and Development of Coastal Sea Fog along the Shandong Peninsula: A Case Study

et al. 2022

Self Cite

View full text Add to dashboard Cite

Among the China Seas, the Yellow Sea has the highest occurrence frequency of sea fog that can be categorized into widespread sea fog and coastal sea fog. In this paper, we study a typical coastal sea fog along the Shandong Peninsula that is accompanied by sea-land breezes. Based on a series of numerical experiments conducted by the Weather Research and Forecasting model with high spatial resolution, the impacts of the sea–land breezes on the formation and development of sea fog are investigated in detail. The land breeze can act as an accelerator that enhances humidification and cooling near the coast which promotes sea fog formation during the nighttime, resulting from nearshore vapor convergence and offshore cool-air transportation jointly by the descending branch and low-level breeze of the land breeze circulation. During the daytime, the sea breeze acts as a reducer that inhibits sea fog development, including the contraction of fog area caused by onshore advection of the sea breeze on cloud liquid water, and the restraint of fog vertical growth due to warming and drying by the descending branch of the sea breeze circulation.

show abstract

Section: Discussionmentioning

confidence: 99%

Impacts of Sea–Land Breeze Circulation on the Formation and Development of Coastal Sea Fog along the Shandong Peninsula: A Case Study

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The control variables in BE include stream function, velocity potential, temperature, surface pressure, and pseudo-RH. Since the cross-correlations among control variables is important to sea fog modeling (Gao and Gao 2020), in the case study of Section 5, we compared the impacts of two kinds of BE covariances (CV5 and CV6) defined by the WRFDA module. The correlations between moisture (i.e., pseudo-RH) and other control variables exist in CV6, but not in CV5.…”

Section: Model Configurationmentioning

confidence: 99%

“…As mentioned in Section 3.2 about BE (CV5 and CV6), the multivariate cross-correlations among control variables in BE are important to sea fog modeling (Gao and Gao 2020). The aforementioned experiments employed CV5, which has no cross-correlation between moisture and other control variables, such as temperature.…”

Section: Sensitivity Experimentsmentioning

confidence: 99%

A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea

Gao

Gao²,

Li³

et al. 2023

Front. Earth Sci.

Self Cite

View full text Add to dashboard Cite

Numerical forecast of sea fog is very challenging work because of its high sensitivity to model initial conditions. For better depicting the humidity structure of the marine atmospheric boundary layer (MABL), Wang et al. (2014) assimilated satellite-derived humidity from sea fog at its initial stage over the Yellow Sea (W14 method), using an extended three-dimensional variational data assimilation (3DVAR) with the Weather Research and Forecasting model (WRF). This article proposes a revised version of the W14 method. The major ingredient of the revision is the inclusion of a temperature constraint into the satellite-derived humidity, not only for the missed fog area that the W14 method primarily considers, but also for the false fog area that is not handled in the W14 method. The numerical experiment results of 10 sea fog cases over the Yellow Sea show that the revised method can effectively alleviate the wet bias occasionally occurring in the W14 method, resulting in an improvement by about 15% for an equitable threat score of the simulated fog area. In addition, a detailed case study is conducted to illustrate the working mechanism of the revised method, including sensitivity experiments focusing on the roles of two kinds of background error covariances (CV5 and CV6) in the assimilation by the WRF-3DVAR. The results suggest that CV6 with multivariate cross-correlation is probably more beneficial to the revised method’s performance.

show abstract

“…The spatial extent of the area influenced by the analysis increment is shown in Figure 4. Gao and Gao (2020) have presented impacts of multivariate B matrix using the CV6 on the 3DVAR DA system of WRF. A dominant-negative correlation is visible in the temperature and WVMR variables.…”

Section: Single Observation Testsmentioning

confidence: 99%

Impact of assimilating lidar water vapour and temperature profiles with a hybrid ensemble transform Kalman filter: Three‐dimensional variational analysis on the convection‐permitting scale

Thundathil

Schwitalla

Behrendt

et al. 2021

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

We discuss the analysis impact of the ensemble-based assimilation of differential absorption lidar observed water vapour and Raman lidar observed temperature profiles into the Weather Research and Forecasting model at convection-permitting scale. The impact of flow-dependent background error covariance in the data assimilation (DA) system that uses the hybrid three-dimensional variational (3DVAR) ensemble transform Kalman filter (ETKF) was compared to 3DVAR DA. The 3DVAR-ETKF experiment resulted in a 50% lower temperature and water vapour RMSE than the 3DVAR experiment when taking the assimilated lidar data as reference and 26% (38%) lower water vapour (temperature) RMSE when comparing against independent radiosonde observations collocated with the lidar site. The planetary boundary-layer height of the analyses compared to independent ceilometer data provided additional evidence of improvement. The 3DVAR analysis RMSE showed 140 m, whereas 3DVAR-ETKF showed 60 m. Although limited to a single case study, we attribute these improvements to the flow-dependent background error covariance matrix in the 3DVAR-ETKF approach. The vertical profile measured from a single stationary lidar system established a spatial impact with a 100 km radius. This seems to indicate future assimilation of water vapour and temperature data from an operational lidar network. The assimilation impact persisted 7 hr into the forecast time compared with the ceilometer data and 4 hr with GPS observations.

show abstract

Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

Cited by 7 publications

References 53 publications

Impacts of Sea–Land Breeze Circulation on the Formation and Development of Coastal Sea Fog along the Shandong Peninsula: A Case Study

Impacts of Sea–Land Breeze Circulation on the Formation and Development of Coastal Sea Fog along the Shandong Peninsula: A Case Study

A revised method with a temperature constraint for assimilating satellite-derived humidity in forecasting sea fog over the Yellow Sea

Impact of assimilating lidar water vapour and temperature profiles with a hybrid ensemble transform Kalman filter: Three‐dimensional variational analysis on the convection‐permitting scale

Contact Info

Product

Resources

About